def plot(request):
x = float(request.POST['x'])
if ('clear' in request.POST) or\
('x' not in request.session) or\
x != request.session['x']:
request.session['x'] = x
request.session['n_x'] = 0
request.session['d_x'] = 1
ax, n = plot_bm_with_last_zero_lt(x)
request.session['n_x'] += 1
request.session['d_x'] += n
output = cStringIO.StringIO()
plt.savefig(output, bbox_inches='tight')
img_txt = base64.b64encode(output.getvalue())
prob_x = arcsine.cdf(x)
n_x = request.session['n_x']
d_x = request.session['d_x']
response = {
'image': img_txt,
'prob': '%.4f' % prob_x,
'frq': '%.4f' % (float(n_x) / d_x),
'n_x': n_x,
'd_x': d_x
}
return HttpResponse(json.dumps(response), content_type='application/json')
def getCDF(self, points=None):
"""
A Chebyshev cumulative density function.
:param Chebyshev self:
An instance of the Chebyshev class.
:param points:
Matrix of points for defining the cumulative density function.
:return:
An array of N values over the support of the Chebyshev (arcsine) distribution.
:return:
Cumulative density values along the support of the Chebyshev (arcsine) distribution.
"""
if points is not None:
return arcsine.cdf(points)
else:
raise (ValueError, 'Please digit an input for getCDF method')
def getCDF(self, points=None):
"""
A Chebyshev cumulative density function.
:param Chebyshev self:
An instance of the Chebyshev class.
:param points:
Matrix of points for defining the cumulative density function.
:return:
An array of N values over the support of the Chebyshev (arcsine) distribution.
:return:
Cumulative density values along the support of the Chebyshev (arcsine) distribution.
"""
if points is not None:
return arcsine.cdf(points)
else:
raise(ValueError, 'Please digit an input for getCDF method')
x = np.linspace(arcsine.ppf(0.01),
arcsine.ppf(0.99), 100)
ax.plot(x, arcsine.pdf(x),
'r-', lw=5, alpha=0.6, label='arcsine pdf')
# Alternatively, the distribution object can be called (as a function)
# to fix the shape, location and scale parameters. This returns a "frozen"
# RV object holding the given parameters fixed.
# Freeze the distribution and display the frozen ``pdf``:
rv = arcsine()
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = arcsine.ppf([0.001, 0.5, 0.999])
np.allclose([0.001, 0.5, 0.999], arcsine.cdf(vals))
# True
# Generate random numbers:
r = arcsine.rvs(size=1000)
# And compare the histogram:
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
ax.legend(loc='best', frameon=False)
plt.show()